17710 results about "Software" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

An example operation may include one or more of receiving context of a user during an inquiry of a feature via a software application, executing a waiting period via the software application, during the waiting period, selecting an animation to display via the software application based on the context of the user and the feature inquiry wherein the animation provides contextual data associated with the feature, wherein the contextual data is based on a determined need of the user, displaying the animation via the software application during the waiting period, and determining if the user has accepted the feature via the software application. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of training an artificial intelligence (AI) model based on a plurality of dashboards related to a software application that corresponds to a plurality of topics, ingesting a call transcript from a previous call with a user, generating a new topic from the call transcript, determining that the new topic is distinct from the existing plurality of topics, executing the AI model based on the new topic, generating a dashboard with content based on the execution of the AI model, and displaying the dashboard via the software application.

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of registering a user for a first feature by a software application, wherein the registering of the user comprises receiving information about the user, determining that the received information does not meet a first condition related to a first feature, determining that the received information meets a second condition related to a second feature, generating an offer to the user the second feature based on the information, detecting acceptance of the second feature, by the software application, and enabling the second feature by the software application for the user in response to the acceptance. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation includes one or more of rendering a graphical user interface within a software application including a plurality of elements, modifying locations of the plurality of elements within the graphical user interface based on user inputs on the graphical user interface, generating a dynamic mapping of the graphical user interface including the modified locations of the plurality of elements based on an execution of an artificial intelligence (AI) model on the rendered graphical user interface, and storing the dynamic mapping of the graphical user interface within a storage.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

Method and system for analyzing software or firmware of computing systems to assess security properties includes loading predicate device input data including characteristics about predicate devices; translating predicate device input data into predicate device model data describing characteristics or dependencies of the predicate device input data relevant to the analysis; determining digital twin configuration data used to configure digital twin; loading the digital twin configuration data onto the digital twin; storing configuration data in the memory; instructing the digital twin to configure itself to implement the loaded digital twin configuration data; determining security analysis to be carried out on the digital twin; simulating the predicate device; executing security analysis on the digital twin; generating output data describing the result of execution of the security analysis; storing output data pertaining to the result; and determining if the result satisfies a predetermined condition, and if so, executing action corresponding to the result.

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

Disclosed are systems and methods that ingest substantial volumes of data from a variety of sources associated with a computing system network to create integrated, intuitive, efficient, and coherent adaptive workspace display interfaces. The ingestion includes event data generated by event source software applications running on end user computing devices as well as call and end user attribute data that is used to determine state, presence, and performance data for the end user computing devices that is formatted for display on the adaptive workspace interfaces and used during performance monitoring and collaboration between system end users. The adaptive workspace interfaces provide functions that facilitate real time collaboration between end users that enhances shared experiences between system end users and customers.

The invention discloses an intelligent definition method and system for industrial edge data acquisition, a medium and equipment. The method comprises the following steps: acquiring operation data of field equipment in real time; performing protocol analysis and cleaning on the data to generate standardized data points, dynamically detecting the communication state and adaptively adjusting the acquisition frequency; classifying and aggregating the standardized data points into a running state feature set and calculating feature indexes; edge side anomaly detection is carried out based on the feature set, and a protocol container library is synchronously called to match a device communication protocol to generate matching information; an acquisition strategy optimization instruction is generated in combination with the abnormal result and the matching information, and transmission characteristic indexes and instructions are packaged; and structuring storage feature indexes according to equipment types and time dimensions to form a historical operation database, and managing a storage period by adopting a sliding window mechanism. According to the invention, protocol adaptive analysis, dynamic acquisition adjustment and edge intelligent analysis are realized through software definition, the hardware dependence and field debugging risk are reduced, and the data acquisition efficiency and the intelligent level are improved.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

A distributed generative artificial intelligence (AI) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative AI models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

Systems and methods for protecting computing systems against ransomware attacks using AI-generated virtual file honeypots. Generative AI comprising a large language model generates virtual file honeypots automatically in response to attack vectors associated with suspect actors and ransomware families.

The invention discloses a software multi-agent collaboration method and system based on a large language model, and the method comprises the steps: receiving natural language task description submitted by a user at the same time, carrying out the semantic understanding and intention recognition through a pre-trained large language model center, and generating a structured task element set; based on the structured task element set, the large language model center generates a task dependency graph through multiple rounds of reasoning, and the task dependency graph comprises a plurality of atomic subtasks, logic relations among the tasks and data flow constraints; according to a topological structure and resource demand characteristics of a task dependency graph, a double-layer graph attention network is adopted to dynamically match a professional agent with specific domain capability, and a distributed collaborative network is formed. Through the dynamic graph network scheduling and cross-domain semantic alignment mechanism, the problems that the multi-agent dynamic collaborative adaptation capability is insufficient and cross-domain semantic fusion is difficult are solved.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention discloses an intelligent risk identification and self-adaptive repair method, system and device for a software supply chain and a medium, belongs to the field of network security and automatic software engineering, and aims to solve the technical problem of how to accurately and comprehensively identify software code supply chain risks including code snippets. A reliable and efficient automatic closed-loop repair scheme is provided, and the technical defects that in the prior art, the software code supply chain recognition range is limited, the repair process is rigid and the reliability is low are overcome. Analyzing the declarative dependency; meanwhile, semantic traceability based on artificial intelligence is carried out on the code snippets, and a global software material list is generated; and performing intelligent mapping on the software components in the global software bill of materials and the vulnerability database to identify risks.

The invention relates to the technical field of process parameter design, and discloses a silver electrolysis process parameter design method and system based on multi-objective optimization. The method comprises the following steps: performing differential time sequence parameter decomposition on operation data of the silver electrolysis process to obtain a sensitive parameter set and a reference parameter set; performing reweighted periodic feature extraction on the sensitive parameter set and the reference parameter set, and establishing a dynamic mathematical model of the silver electrolysis process parameters; based on the dynamic mathematical model of the silver electrolysis process parameters, physical and chemical process numerical simulation is conducted through COMSOLMultiphysics software, and a response curved surface model is obtained; and according to the response curved surface model, a process parameter combination evaluation value sequence is generated, silver electrolysis process parameter multi-objective optimization is carried out based on the process parameter combination evaluation value sequence, and a silver electrolysis process parameter optimization scheme is output. According to the application, multi-physical-field collaborative simulation of electrode reaction, ion transmission, heat conduction and the like is realized, and performance index responses under different process parameters are accurately predicted.

A distributed indexing and resolution architecture is disclosed for decentralized systems requiring modular, trust-scoped mutation control and dynamic alias governance. The system comprises a plurality of index entries arranged in a parent-child hierarchy, each associated with a structured alias and governed by one or more anchor nodes. Anchors perform localized resolution, mutation validation, and restructuring operations under deterministic policy constraints, enabling semantic scope enforcement and entropy-sensitive adaptation without requiring global consensus or centralized control. The architecture supports scoped alias traversal, asynchronous mutation proposals, and elastic anchor registration based on system state metrics. The indexing substrate may be integrated into heterogeneous infrastructures, including systems comprising distributed software agents, semantic execution platforms, or pseudonymous identity frameworks. Anchors coordinate within defined trust domains to ensure lineage continuity, dynamic rekeying, and semantic integrity across independently governed segments of a decentralized namespace.

Systems, methods, and computer-readable storage mediums for generating a user interface and user experience (UI / UX). The method comprises receiving user interaction data from multiple sources and creating one or more UI / UX elements and associated content based on the received user interaction data. The method also comprises receiving user response to the one or more UI / UX elements and the associated content; modifying at least one of the one or more UI / UX elements based on the received user response; and generating the UI / UX based on the modification.

The present disclosure relates to transportation and related methods and systems including software-defined vehicles, vehicle operating states, an identity management system, an intelligent digital twin system that creates, manages, and provides digital twins for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

Systems, methods, and software are disclosed herein for a system of agents for managing tasks of software applications which is guided by generative AI. In an implementation, a computing apparatus determines that a task has been assigned to an application assistant of an application. The application assistant includes multiple agents which interact with a generative AI model. The computing apparatus orchestrates the multiple agents in their interactions with the generative AI model in furtherance of completing the task and updates the contextual information of the task based on the interactions.

The invention relates to the technical field of intelligent operation and maintenance, in particular to an execution method of a large model graph retrieval enhancement system oriented to software and hardware monitoring operation and maintenance, which comprises the following steps: S1, inputting fault information to the graph retrieval enhancement system; s2, a graph construction and updating module receives and processes the fault information, generates a triple, and writes the triple into an operation and maintenance / fault knowledge graph; s3, the fault information is input into a graph retrieval enhancement module for two-stage filtering retrieval, and sub-graph information is screened out; s4, the prompt word construction module converts the fault information and the screened sub-graph information into structured natural language prompt segments; and S5, a reasoning generation module performs natural language question and answer to generate a fault analysis result. Based on the above scheme, the execution method enhances the adaptive capacity of knowledge reasoning and fault positioning, gives play to the generalization reasoning capacity of a large language model while ensuring the accuracy, and enhances the practical value.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.